MétaCan
Menu
Back to cohort
Record W2802484730 · doi:10.7939/r3tq5rt5q

Finite Element Modeling of Thermal Insulation Effects in a Borehole Thermal Energy Storage

2017· article· en· W2802484730 on OpenAlexaboutno aff
Jungjin Lee

Bibliographic record

VenueUniversity of Alberta Library · 2017
Typearticle
Languageen
FieldEnergy
TopicGeothermal Energy Systems and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsBoreholeThermalFinite element methodThermal energy storageThermal insulationEnvironmental scienceGeologyGeotechnical engineeringMaterials scienceEngineeringStructural engineeringComposite materialPhysicsMeteorologyThermodynamics

Abstract

fetched live from OpenAlex

A recent application of borehole thermal energy storage (BTES) technology to residential properties in Canada shows a significant reduction in the use of natural gas thereby saving energy consumption and reducing the generation of greenhouse gases. However, due to the construction principles of the BTES, the systems are not thermally insulated on the sides and the bottom. Hence, almost all injected heat into a single borehole dissipates into surrounding ground over the night when heat injection stops. In order to minimize thermal energy dissipation, construction of thermal insulation barrier using expanded perlite aggregate (EPA) was proposed to reduce the heat flow and increase the efficiency by providing a soilcrete thermal insulation layer around the BTES system. The initial research proposed to utilize jet grouting technology for construction of the EPA mixed soilcrete thermal insulation layer. However, due to the nature of the jet grouting technology, the construction process of jet grouting is lengthy and therefore expensive for this application. Besides, the high buoyancy forces exerted created potential risks of aggregate segregation. To improve constructability and to mitigate the risks of aggregate segregation, this research proposes to employ one pass deep trenching method construction of the soilcrete thermal insulation layer.the risks of aggregate segregation, this research proposes to employ one pass deep trenching method construction of the soilcrete thermal insulation layer. Full-scale numerical models using two finite element analysis (FEA) software: Abaqus and Temp/W were developed to investigate the effectiveness of the soilcrete insulation layer constructed with one pass deep trenching method. The numerical model provides theoretical evidence for the application of soilcrete thermal insulation layer in reducing the thermal energy loss and thereby improving the efficiency of the system. The FEA modeling results showed that the thermal insulating soilcrete successfully entrapped more thermal energy within the system compared to the system without thermal insulation and reduced the annual average heat flux up to 46 % with three-meter thickness insulation barrier.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.091
Threshold uncertainty score0.932

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.009
GPT teacher head0.180
Teacher spread0.171 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueUniversity of Alberta LibrarySame topicGeothermal Energy Systems and ApplicationsFrench-language works237,207